3D automated lymphoma segmentation in PET images based on cellular automata

Abstract

Positron Emission Tomography imaging (PET) has today become a valuable tool in oncology. The accurate definition of the tumor volume on PET images is a critical step. State-of-the-art methods are based on adaptative thresholding and usually require user interaction. Their performances are hampered by the low contrast, low spatial resolution, and low signal to noise ratios of PET images. In this paper, we investigate an automated segmentation approach based on a cellular automata algorithm (CA). The method's performance is evaluated against manual delineation on PET images obtained from clinical data. Our method obtains encouraging results as compared to standard interactive PET segmentation algorithms.